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Resampling methods for meta-model validation with recommendations for evolutionary computation.

B Bischl1, O Mersmann, H Trautmann

  • 1Faculty of Statistics, TU Dortmund University, Dortmund, Germany. bischl@statistik.tu-dortmund.de

Evolutionary Computation
|February 21, 2012
PubMed
Summary
This summary is machine-generated.

Meta-modeling aids expensive optimization by building accurate fitness function models. This study emphasizes rigorous model validation using resampling techniques to avoid common pitfalls in evolutionary optimization.

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Area of Science:

  • Computational Science
  • Optimization Theory
  • Statistical Modeling

Background:

  • Meta-modeling is essential for computationally expensive optimization problems.
  • Previous research focused on regression techniques for fitness function approximation.
  • Accurate model assessment is critical but often overlooked in meta-modeling.

Purpose of the Study:

  • To highlight the importance of model accuracy assessment in meta-modeling.
  • To systematically discuss resampling strategies for model validation.
  • To provide practical insights into meta-modeling for evolutionary optimization.

Main Methods:

  • Survey of meta-modeling techniques in evolutionary optimization.
  • Systematic discussion of resampling strategies (cross-validation, bootstrapping, etc.).
  • Presentation of practical examples illustrating pitfalls in model selection and assessment.

Main Results:

  • Model validation is a crucial, yet often underestimated, aspect of meta-modeling.
  • Various resampling techniques have distinct features, benefits, and potential pitfalls.
  • Practical examples demonstrate common errors in model selection and performance evaluation.

Conclusions:

  • Choosing appropriate model validation techniques is vital for reliable meta-modeling.
  • Understanding the nuances of resampling methods prevents common errors.
  • This work provides guidance for effective meta-modeling in optimization settings.